How do you run local embeddings for private search?
. Sources checked November 3, 2026.
Run a small embedding model on your own machine, embed your documents locally, store the vectors locally, and search them locally, so the documents never leave the machine. An embedding is a list of numbers that represents the meaning of a piece of text. Private search works by embedding every document once, embedding the query the same way, and returning the documents whose embeddings are closest to the query's embedding. When all three steps happen locally, the documents and the query do not need to be sent to an outside service.
What an embedding model does
An embedding model converts text into a vector, a list of numbers of fixed length, such that texts with similar meaning have vectors that are close together. That closeness is usually measured by cosine similarity, which compares the direction of two vectors rather than their exact values. A search system does not need to understand the text the way a chat model does; it only needs to compare vectors and return the closest documents.
Embedding models are usually much smaller than chat models, and many run well on a CPU or on a modest GPU. That small size is what makes local private search practical on a home machine. A model that is too large for the task adds latency without improving search quality, while a model that is too small may not distinguish between similar documents. Choose a model that was designed for embeddings, not a chat model pressed into service as an embedder, unless the runtime documents that use.
The embedding for a document and the embedding for a query must come from the same model, with the same settings. If documents were embedded with one model and the query is embedded with another, the vectors are not comparable, and the search results will be meaningless even if the system returns documents. Keep the model name, version, and any instruction prefix in the search system's configuration, so a model change triggers a re-embedding of the documents.
Building a local index
First, split documents into chunks that are small enough for the embedding model to handle, but large enough to carry meaning. A chunk that is too long will be truncated by the model, and a chunk that is too short may not contain enough context to match a query. Keep the source document name and the chunk's position with each vector, so a search result can be traced back to the exact passage it came from.
Second, embed each chunk locally and store the vector with its source information in a local vector store or in a simple file-based index. The store does not need to be a separate server; for a personal collection, a local file or a local database is enough. What matters is that the vectors, the chunks, and the source mapping are all on the same machine and are backed up together, because a vector without its source text is useless.
Third, embed the query with the same model and compare it to the stored vectors. Return the closest chunks, with their source names, and show the matching passage to the user. Do not return only a document title; a private search is useful when the user can see why a document matched, in the passage itself.
Fourth, test the index with questions you know the answer to. If a known document does not appear for its own keywords, check the chunking, the model, and whether the query used an instruction prefix that the documents did not. A search system that has never been tested with known questions is not ready to be trusted with unknown ones.
Keeping search private
Private means the documents, the embeddings, and the queries stay on the machine the user controls. That requires a local runtime, a local vector store, and no outside API call in the search path. A system that embeds locally but sends the query to an outside service for ranking is not private search, even if the documents were embedded locally.
Also check the runtime's defaults. Some local runtimes phone home for updates, telemetry, or model downloads, and some tools send usage data unless that setting is changed. Turn off any outside reporting for the search system, and verify with network monitoring if privacy is a requirement, not just a preference. A claim of privacy that has not been checked against the actual network traffic is not a guarantee.
Back up the index with the documents. An index that took hours to build from a large collection should be backed up like any other data, with the model name and version recorded, so it can be rebuilt if the machine fails. If the model changes, re-embed the documents rather than mixing vectors from two models in one index.
Limits of local embeddings
Local embeddings do not understand a document the way a person does, and they can return a document that shares words with the query but not meaning, or miss a document that uses different words for the same idea. That is a limit of the method, not a bug in a particular model. Improve results by better chunking, by adding the document title to the chunk, and by letting the user refine the query, rather than by assuming a larger model will fix every miss.
Local search also does not replace a chat model for answering questions. Embeddings find passages; a chat model, if used, reads those passages and composes an answer. Keeping those steps separate makes the system easier to debug: if the passages are wrong, the embedding or chunking is wrong; if the passages are right but the answer is wrong, the chat step is wrong.
Finally, do not embed documents you do not have the right to store, and do not share an index that contains private documents as if it were public data. The vectors themselves can leak information about the documents, so treat an index with the same care as the documents it came from.
Frequently asked questions
Do documents leave the machine in local embedding search?
No, if the embedding model, the vector store, and the query handling all run locally, with no outside API call in the search path.
Must the query use the same model as the documents?
Yes, document vectors and query vectors are only comparable when they come from the same model with the same settings.
Can a small embedding model run on a CPU?
Yes, many embedding models are small enough to run well on a CPU, which is what makes local private search practical on a home machine.
Sources
This page is general information, not legal advice. Check the linked official source for the rule that applies to a specific home.
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